This work presents a computational approach to analyze character development along the narrative timeline.The analysis characterizes changes in the protagonist's views and behavior and the interplay between them.We consider transcripts of Holocaust survivor testimonies as a test case, each telling the story of an individual in first-person terms.We focus on the survivor's religious trajectory, examining the evolution of their disposition toward religious belief and practice as it is reflected in the testimony.Clustering the resulting trajectories in the dataset, we identify common sequences in the data.Our findings highlight multiple common structures of religiosity across the narratives: in terms of belief, a constant disposition is common, while for practice, most present an oscillating structure, serving as valuable material for historical and sociological research.This work demonstrates the potential of natural language processing for analyzing character evolution through thematic trajectories in narratives.

This work focuses on the spatial dimension of narrative understanding and presents the task of event-location tracking in narrative texts, namely the extraction of the sequence of locations where the narrative is set.We present several architectures for the task that seek to model the global structure of the sequence, with varying levels of context awareness.We compare these methods to a number of strong baselines and ablated variants.We also develop methods for the generation of location embeddings and show that learning to predict a sequence of continuous embeddings is advantageous in terms of performance over predicting a string of locations.We focus on the test case of Holocaust survivor testimonies, motivated by the moral and historical importance of studying this dataset using computational means.The dataset further provides a unique case of a large set of narratives with a relatively restricted set of location trajectories.Our results show that models that are aware of the global context of the narrative can generate more accurate location chains.We corroborate the effectiveness of our methods by showing similar trends in an additional domain. 1

We develop computational models to analyze court statements in order to assess judicial attitudes toward victims of sexual violence in the Israeli court system. The study examines the resonance of "rape myths" in the criminal justice system's response to sex crimes, in particular in judicial assessment of victim's credibility. We begin by formulating an ontology for evaluating judicial attitudes toward victim's credibility, with eight ordinal labels and binary categorizations. Second, we curate a manually annotated dataset for judicial assessments of victim's credibility in the Hebrew language, as well as a model that can extract credibility labels from court cases. The dataset consists of 855 verdict decision documents in sexual assault cases from 1990-2021, annotated with the help of legal experts and trained law students. The model uses a combined approach of syntactic and latent structures to find sentences that convey the judge's attitude towards the victim and classify them according to the credibility label set. Our ontology, data, and models will be made available upon request, in the hope they spur future progress in this judicial important task.

The task of topical segmentation is well studied, but previous work has mostly addressed it in the context of structured, well-defined segments, such as segmentation into paragraphs, chapters, or segmenting text that originated from multiple sources.We tackle the task of segmenting running (spoken) narratives, which poses hitherto unaddressed challenges.As a test case, we address Holocaust survivor testimonies, given in English.Other than the importance of studying these testimonies for Holocaust research, we argue that they provide an interesting test case for topical segmentation, due to their unstructured surface level, relative abundance (tens of thousands of such testimonies were collected), and the relatively confined domain that they cover.We hypothesize that boundary points between segments correspond to low mutual information between the sentences proceeding and following the boundary.Based on this hypothesis, we explore a range of algorithmic approaches to the task, building on previous work on segmentation that uses generative Bayesian modeling and state-of-the-art neural machinery.Compared to manually annotated references, we find that the developed approaches show considerable improvements over previous work.1